One way to explain about Industrial Engineering is “Scientific Management” from Taylorism at the time of the 1st Industrial Revolution. We believe that the scientific management can be only possible through
mathematics, computers and data (collection, analysis, modeling, applications and management).
We are all problem-solver. If there are problems in any domain (big data, data mining, deep learning, computational intelligence, biology, nano-science, micro-technology, information technology, telecommunications, medical areas, shipbuilding, additive manufacturing, and traditional areas including manufacturing, logistics, supply chain and inventory), we figure out how to solve those problems (see Prof. Lee’s achievements and research areas).
We study approaches. Many engineering disciplines study the problem domain itself without knowing how to systematically model a complex system, analyse it mathematically, develop an effective and efficient algorithm, use various computation tools, and finally solve the problem.
We improve and optimize complex systems. Many people use the word “optimization” without what it means. “Improve” is different from “Optimize” as “Do your best” is different from “Be the best”. We find the best or near-optimal solutions as problems required. We make any system more effectively and efficiently using the scientific approaches (math, computers, and data).
We support decision-makings. We have the philosophy and capability to see through the problems and analyse them critically. They makes us different from people in other areas of Industrial Engineering. We can build an abstract model from complex problems and generate supportive solutions for the important decision makings.
We are all problem-solver. If there are problems in any domain (big data, data mining, deep learning, computational intelligence, biology, nano-science, micro-technology, information technology, telecommunications, medical areas, shipbuilding, additive manufacturing, and traditional areas including manufacturing, logistics, supply chain and inventory), we figure out how to solve those problems (see Prof. Lee’s achievements and research areas).
We study approaches. Many engineering disciplines study the problem domain itself without knowing how to systematically model a complex system, analyse it mathematically, develop an effective and efficient algorithm, use various computation tools, and finally solve the problem.
We improve and optimize complex systems. Many people use the word “optimization” without what it means. “Improve” is different from “Optimize” as “Do your best” is different from “Be the best”. We find the best or near-optimal solutions as problems required. We make any system more effectively and efficiently using the scientific approaches (math, computers, and data).
We support decision-makings. We have the philosophy and capability to see through the problems and analyse them critically. They makes us different from people in other areas of Industrial Engineering. We can build an abstract model from complex problems and generate supportive solutions for the important decision makings.
